Ask stories

What are you working on? Any new ideas that you're thinking about?

Seems like xAI is having a Fairchild semiconductor moment, saw at least half a dozen people posting they are leaving xAI, and seems like all of them are starting something new, together.

- https://x.com/Chace_AGI/status/2021452881875141060 - https://x.com/jimmybajimmyba/status/2021374875793801447 - https://x.com/Yuhu_ai_/status/2021113745024614671 - https://x.com/C_S_Skeptic/status/2021457245805109750?s=20

I found myself increasingly outsourcing the details to the AI. I forgot the details, deliberately I think. I wanted the AI to know them. Why? Because that's where the compute is. So that's where the knowledge has to live.

Me re-telling it to the AI every time it misses something it didn't know, is inefficient. It takes me X time to type it, and maybe log(X) to voice type it. But then there's the inevitable back and forth, the slight misunderstandings, the corrections, etc.

I realized and found myself naturally sliding down towards, just letting the AI own all the data and knowledge. Becuase it should. That's the one that has to compute with it, so why should I know about it.

People think AI is a slippery slope by outsourcing our thinking. I don't think it's a slippery slope - it's a waterslide. It's just inevitable. It's gravity, taking over rapidly. Because there's no force nor incentive pushing the water back up hill.

AI should own all the data. As weird as that feels, that's how it should be. I don't know another way right now.

Thoughts?

We've been going through enterprise sales processes with both of you, and I've encountered some practices I haven't seen before with other B2B vendors:

Usage data availability:

We're being told we can't access usage data for our existing accounts unless we sign a 12-month commitment. We need this data to make an informed purchasing decision.

Pricing validity:

Received a pricing link with 14-day validity. On day 13, we were told pricing had doubled and the original quote wouldn't be honored.

I understand AI is a fast-moving market and everyone's scaling rapidly. But these create real trust issues for procurement teams trying to make informed decisions.

Has anyone else experienced similar challenges with AI vendor negotiations? I'm hoping these are isolated issues rather than emerging patterns.

I am a solo founder building in AI B2B infra.

I am filing provisional patents on some core technical approaches so I can share more openly with early design partners and investors.

Curious from folks who have raised Pre-Seed/Seed or worked with early-stage companies: - Do provisionals meaningfully help in fundraising or partnerships? - Or were they mostly noise until later rounds / real traction?

I am trying to calibrate how much time/energy to put into IP vs just shipping + user traction at this stage.

Would love to hear real world experiences.

I was a Cursor Pro subscriber. On January 14th, I hit my subscription usage limit. No warning. No "Hey, you've used up your included quota — want to keep going at per-token rates?" Cursor just... kept going. Silently switched me to what they call "On-Demand" billing — meaning every single token I used from that point was billed at API rates. And I had no idea. "On-Demand usage" — who interprets that as post-paid charges? Here's what gets me. I've lived in the US for years. My English is fine. But when I saw "On-Demand usage" in my account, I genuinely thought it meant usage within my subscription plan — as in, I'm using it on demand, whenever I need it. You know, like on-demand streaming. On-demand services. That's what the phrase means in literally every other context. It does not mean that here. In Cursor's world, "On-Demand" means "you are now being charged per token at full API pricing and we will bill you later." If that's not deliberately misleading terminology, it's at minimum terrible UX design. How I blew through $20 in 4 days without knowing The claude-4.5-opus-high-thinking model costs $0.50–$4.00+ per request. I didn't know I was on per-token billing, so I kept using it like normal. Four days. $20 gone. When I was finally blocked, I thought my subscription limit had just run out late. The UI prompted me to "add API usage" for $20, so I did — thinking it was a top-up balance I could draw from. Nope. Cursor support later told me:

"These aren't prepayments or top-ups — they're charges for API usage that already happened."

So I wasn't adding credit. I was raising a spending cap on charges that had already been silently accumulating. The UI gave me zero indication of this. The bill ItemAmountPro subscription~$20On-Demand charge #1 (Jan 18)$20On-Demand charge #2 (Feb 7)$20Total for ~2.5 weeks~$60 The final invoice showed $42.12 in total On-Demand usage. After subtracting the first $20 payment and a $2.12 refund for exceeding the hard limit, I was charged another $20. Support made it worse I emailed asking for a refund. Denied. Fine — I used the tokens, I accept that. But here's what I can't accept: support misrepresented the charges. They told me the Feb 7th charge was for "17 calls to gpt-5.1-codex-max totalling $0.29" with a "$20 minimum charge applied." That made it sound like I was charged $20 for 29 cents of usage. That's not what happened at all. The $20 was the remaining balance of $42.12 across multiple models. Why frame it that way? Either support doesn't understand their own billing, or they were trying to shut down my refund request with a misleading explanation. What Cursor needs to fix

Hard-stop when subscription limit is reached. Don't silently switch to per-token billing. Ask the user. Get explicit consent. This is basic. Rename "On-Demand usage." Nobody interprets this as "post-paid per-token charges." Call it what it is: "Pay-per-use billing" or "Overage charges." Be honest. Make "Add API credit" actually work like credit. If I click a button that says I'm adding $20, I expect a $20 balance. Not a silent spending cap increase on charges I didn't know existed. Train support to explain billing accurately. Don't cherry-pick one line item to make a $20 charge look like a minimum fee issue when it's actually part of a $42 total.

My advice to Cursor users

Check your billing page constantly. The subscription-to-on-demand switch is invisible. Avoid Opus and high-thinking models unless you're actively monitoring costs. One session can cost $10+. When you see "add API usage," understand you're NOT adding a balance. You're raising a spending limit. If you cancel, watch for charges that show up weeks later.

I'm done with Cursor. The tool itself is fine, but the billing system feels designed to extract maximum revenue through confusion rather than transparency.

Anyone else get caught by this? Genuinely curious if this is a widespread issue or if I'm just the lucky one.

I get it - LLMs do have some value, but not as much as everyone (especially those from AI labs) is trying to pitch. I can't help thinking that it's so obvious we are almost at the very top of this bubble - but here it feels like the majority of HN doesn't think like that...

Yet just in 2026 we had:

- AI.com was sold for $70M - Crypto.com founder bought it to launch yet another "personal AI agent" platform, which promptly crashed during its Super Bowl ad debut.

- MoltBook-mania - a Reddit clone where AI bots talk to each other, flooded with crypto scams and "AI consciousness" posts. 250,000+ bot posts burning compute for what actual value? [0]

- OpenClaw - a "super open-source AI agent" that is a security nightmare.

- GPT-5.3-Codex and Opus 2.6 were released. Reviewers note they're struggling to find tasks the previous versions couldn't handle. The improvements are incremental at best.

I understand there are legitimate use cases for LLMs, but the hype-to-utility ratio seems completely out of whack.

Am I not seeing something?

[0] https://www.technologyreview.com/2026/02/06/1132448/moltbook-was-peak-ai-theater/

I wrote a long analysis about why AI behavior may depend less on model ethics and more on the environment it is placed in — especially evaluation structures (likes, rankings, immediate feedback) versus relationship structures (long-term interaction, delayed signals, correction loops).

The article uses the Moltbook case as a structural example and discusses environment alignment, privilege separation, and system design implications for AI safety.

Full article: https://medium.com/@clover.s/ai-isnt-dangerous-putting-ai-inside-an-evaluation-structure-is-644ccd4fb2f3

I've been steadfastly trying my best to incorporate the latest-and-greatest models into my workflow. I've been primarily using Codex recently. But I'm still having difficulties.

For example: no matter what I do, I can't prevent Codex from introducing linter errors.

I use tabs instead of spaces for indentation. It seems like the model is massively weighted on code written using spaces (duh). Despite having a very well articulated styleguide (that Codex helped me write after examining my codebase!) that clearly specifies that tabs are used for indentation, the model will happily go off and make a bunch of changes that incorporate spaces seemingly at random. It will use tabs correctly in certain places, but then devolve back to using spaces later on in the same files.

I have a linter that I've taught the model to run to catch these things, but A) that feels like such a waste of tokens and B) the model often forgets to run the linter anyway.

It's like having a junior developer who has never used the tab key before. They remember to ctrl-f their spaces->tabs sometimes before opening a PR, but not all the time. So I wind up giving them the same feedback over and over.

This example -- tabs instead of spaces -- is just one specific case where the model seems to invariably converge on a local maximum that is dictated by the center of the software bell curve. But in general I'm finding it to be true of just about any "quirky" style opinion I want to enforce:

- Avoid Typescript's `any` or `unknown`: the model will still happily throw these in to make the compiler happy

- Avoid complex ternaries: nope, the model loves these

- Avoid inscrutable one-letter variables versus longer, self-descriptive ones: nope, `a` it is

It just seems like I'm using a tool that is really good at producing the average software output by the average software developer. It's not able to keep my aesthetic / architectural desires at the front of its thinking as it's making my desired changes. It's as if I hired the guy from Memento as an intern.

How do people get around this, other than slathering on even more admonitions to use tabs no matter what (thereby wasting valuable tokens)?

Beyond syntax, I'm still able to use the models to crudely implement new features or functionality, but the way they write code feels... inelegant. There's often a single unifying insight within grasp that the model could reach for to greatly simplify the code it wrote for a given task, but it's not able to see it without me telling it that it's there.

I’m curious whether others have observed this or if it’s just perception or confirmation bias on my part. I’ve seen discussion on X suggesting that older models (e.g., Claude 4.5) appear to degrade over time — possibly due to increased quantization, throttling, or other inference-cost optimizations after newer models are released. Is there any concrete evidence of this happening, or technical analysis that supports or disproves it? Or are we mostly seeing subjective evaluation without controlled benchmarks?

We do "what are you building?" threads all the time. I want to hear the other side. What's a tool, product, or service you'd actually use that nobody seems to be making? Could be a better version of something that exists, or something totally new.

Is anyone else seeing this?

Hi HN,

I am an orthopedic surgeon and a self-taught developer. I built OrthoRay because I was frustrated with the lag in standard medical imaging software. Most existing solutions were either bloated Electron apps or expensive cloud subscriptions.

I wanted something instant, local-first, and privacy-focused. So, I spent my nights learning Rust, heavily utilizing AI coding assistants to navigate the steep learning curve and the borrow checker. This project is a testament to how domain experts can build performant native software with AI support.

I built this viewer using Tauri and wgpu for rendering.

Key Features:

Native Performance: Opens 500MB+ MRI series instantly (No Electron, no web wrappers).

GPU-Accelerated: Custom wgpu pipeline for 3D Volume Rendering and MPR.

BoneFidelity: A custom algorithm I developed specifically for high-fidelity bone visualization.

Privacy: Local-first, runs offline, no cloud uploads.

It is currently available on the Microsoft Store as a free hobby project.

Disclaimer: This is intended for academic/research use and is NOT FDA/CE certified for clinical diagnosis.

I am evaluating open-source licensing options to make this a community tool. I’d love your feedback on the rendering performance.

Link: https://orthoarchives.com/en/orthoray

I understand Llama 4 was a disappointment, but what's happened at Meta since then? Their API is still waitlist-only 10 months on.

I’ve put together a blog focused on Nova, a web framework built on Erlang and the BEAM VM.

The goal was to create something practical and easy to follow — covering setup, routing, views, plugins, authentication, APIs, and WebSockets — with a focus on how Nova fits into the broader BEAM ecosystem.

Blog: https://taure.github.io/novablog/

Nova repo: https://github.com/novaframework/nova

If you're interested in building fault-tolerant web apps on BEAM (and not just using Phoenix/Elixir), you might find it useful.

Feedback, corrections, and suggestions are welcome.

Why didn’t DSPs evolve toward vector accelerators instead of VLIW, despite having highly regular data-parallel workloads

I wanted to find new hobbies for myself, something that involves more physical stuff compared to only code. How did you started on your journey with robotics, what's handy to learn in the first place? I know only basics about embedded programming and I'd need to brush up of my physics skills. I don't have a set goal in my mind, only exploring for the time being.

I’ve been working on a high-performance, web-based flight simulator as a personal project, and I wanted to share a gameplay preview.

The main goal of this project is to combine high-fidelity local 3D aircraft rendering with global, real-world terrain data. All running directly in the browser with no installation required.

Stack: HTML, CSS, JavaScript, Three.js, CesiumJS, Vite.

The game currently uses multiple states, including a main menu, spawn point confirmation, and in-game gameplay. You can fly an F-15 fighter jet complete with afterburner and jet flame effects, as well as weapon systems such as a cannon, missiles, and flares. The game features a tactical HUD with inertia effects, full sound effects (engine, environment, and combat), configurable settings, and a simple NPC/AI mechanism that is still under active development.

The project is still evolving and will continue to grow with additional improvements and features.

Project page: https://github.com/dimartarmizi/web-flight-simulator

One of the Super Bowl commercials today was from Genspark.ai , a company I had not heard of before today. Their website looks like a generic ChatGPT clone. Their LinkedIn page boasts about their revenue, but doesn't describe what they do in a meaningful way.

Has anyone heard of this product, or used it? Is this anything other than a thin wrapper around another company's LLM agent?

I am curious what you guys use for customer facing analytics. Do you make your own or do you use something like Metabase? What do you like and don't like about it?

The Q1 2026 labor data presents a significant anomaly. We are observing a persistent high-volume layoff cycle (~25k YTD) occurring simultaneously with a projected $5.5T global economic loss attributed to unfilled technical roles (IDC).

This suggests we aren't witnessing a cyclical downturn, but a structural "displacement event" driven by a rotation in capital and compute requirements.

Three observations for discussion:

1. *The Infrastructure Bottleneck:* While application-layer development is being compressed by agentic IDEs and higher-level abstractions, the demand for the "underlying" stack (vector orchestration, GPU cluster optimization, custom RAG pipelines) has entered a state of acute scarcity. 2. *The Depreciation of Mid-Level Generalism:* We are seeing a "Mid-Level Squeeze" where companies prioritize either "AI-Native" entry-level talent (low cost, high adaptability) or Staff-level architects. The traditional 4-8 YOE generalist feature developer appears to be the primary demographic of the current layoff cycle. 3. *The Revenue-to-Engineer Ratio:* For the first time, we are seeing "Agentic" teams of 2-3 engineers maintaining systems that previously required 15-20. This shift isn't just about efficiency; it's about the fundamental unit of labor changing from "writing lines of code" to "orchestrating system logic."

Is the $5.5T "gap" actually fillable by the current workforce, or are we looking at a permanent bifurcation where a large segment of the legacy SWE population becomes structurally unemployable without a complete ground-up retraining in the data/inference pipeline?